Batch processes are used in industrial processes for a variety of industries (e.g., the food and chemical industries). A “batch process”, as used herein, refers to a process that runs for a finite (but variable) duration to produce a batch of product at the end of the duration. The antithesis of a batch process is a continuous process, such as a continuous processes based on a distillation column. Batch processes are often used in industrial processes for manufacturing batches of specialty products with high added value. Such specialty products include, but are not limited to, pharmaceuticals, resins, and composites. Batch processes are also typically used in industrial processes for producing batches of food. Online monitoring of such batch processes is important for safe production of high quality products. Such monitoring approaches are also important to enhance the production efficiencies so that a consistent set of high quality batches are produced.
The inherent time varying nature of batch processes results in a variation of batch conditions throughout the performance of the batch process. The phrase “batch condition”, as used herein, refers to the state (or health) of the product being manufactured during a batch process. The state (or health) of a batch of product can generally be defined in terms of normalcy. For example, a batch condition can indicate a healthy batch of product (i.e., a normal batch of product) or an unhealthy batch of product (i.e., an abnormal batch of product).
There are several methods known in the art for online monitoring of batch processes. Conventional methods generally involve assessing a batch condition at a particular time during the performance of a batch process. This assessment is provided by taking into account an entire course history of a batch process rather than just current conditions of a batch process. This assessment generally involves: (a) collecting data including measured variables obtained during the performance of the batch process (i.e., from a start time t0 to the particular time t); (b) considering each measured variable as a distinct variable; (c) considering a set of measured variables as a collection of variables; and (d) representing the collection of variables as a single vector V. The vector V computation requires the complete batch history, which presents a challenge for online assessment of the state of the batch since the set of measured variables are not fully obtained until the batch process is complete. As a result, the assessment requires the forecasting of future variable measurements. The forecasting of future variable measurements requires filling up unobserved data related to the unperformed portion of the batch process with historical data, i.e., data obtained during a previous performance of a batch process. This forecasting process ensures that the batch conditions of the product being manufactured are compared to archived batch conditions of manufactured products. In effect, the health of the product being manufactured is assessed in a statistical manner.
The conventional methods for online monitoring of batch processes described above suffer from certain drawbacks. For example, historical data is used to forecast future measurements. As a result, there is a possibility of poor prediction of batch evolution, since the use of historical data can bias a decision indicating that the batch condition is normal when in fact the batch condition may be abnormal. In such a scenario, the time between an onset of an abnormal batch condition and its detection is relatively long. Further, conventional methods for online monitoring of batch conditions are generally unable to directly use multivariate statistical analysis tools due to the time varying nature of batch processes.